Using AI to develop early warning systems for flooding
Susanne Nielsen recognizes the growing threat of flooding to her parents’ summer house in North Jutland. With rising groundwater levels posing a risk to the property, Susanne is understandably concerned about the potential impact of heavy rainfall.
To address these concerns, researchers from DTU have collaborated with Jammerbugt Municipality to create an innovative early warning tool. This tool harnesses the power of artificial intelligence to provide localized flood warnings up to 48 hours in advance, focusing on rivers, streams, and coastal areas in the municipality.
The tool, known as the wet index, utilizes freely available data from sources such as satellite imagery, weather forecasts, and ground and seawater levels. By incorporating AI technology, the model behind the wet index can analyze complex interactions between water movement, distribution, and environmental factors.
Initial trials in 2023 demonstrated promising results during wet spring months, offering accurate predictions. However, challenges arose during the drier summer period when the tool inaccurately forecasted flooding due to limited data availability from satellite imagery obscured by vegetation.
Despite these challenges, the wet index shows great potential for enhancing flood preparedness and response efforts. Jammerbugt Municipality is now seeking funding to refine the model and address shortcomings, ensuring its reliability and accuracy.
In addition to providing real-time flood warnings for residents and emergency services, the tool’s applications extend to urban planning and infrastructure development. By leveraging scientific machine learning, researchers aim to streamline the analysis of water movement through drainage systems, enabling faster and more efficient decision-making processes.
Through the integration of cutting-edge technology and data-driven insights, communities like Jammerbugt are better equipped to mitigate the impacts of flooding and adapt to changing climate conditions. The development of early warning systems represents a proactive approach to enhancing resilience and protecting vulnerable areas from the growing threats of climate change.
By embracing innovation and collaboration, municipalities can leverage AI tools to safeguard communities, infrastructure, and natural resources against the increasingly severe impacts of extreme weather events. The future of flood preparedness lies in the intersection of technology, data, and community engagement, setting a precedent for proactive disaster response and risk management strategies.
